research-article

Fourier-assisted machine learning of hard disk drive access time models

Authors:

Carlos Maltzahn,

Thomas Kroeger,

Ron OldfieldAuthors Info & Claims

PDSW '13: Proceedings of the 8th Parallel Data Storage Workshop

Pages 45 - 51

https://doi.org/10.1145/2538542.2538561

Published: 17 November 2013 Publication History

Abstract

Predicting access times is a crucial part of predicting hard disk drive performance. Existing approaches use white-box modeling and require intimate knowledge of the internal layout of the drive, which can take months to extract. Automatically learning this behavior is a much more desirable approach, requiring less expert knowledge, fewer assumptions, and less time. Others have created behavioral models of hard disk drive performance, but none have shown low per-request errors. A barrier to machine learning of access times has been the existence of periodic behavior with high, unknown frequencies. We show how hard disk drive access times can be predicted to within 0:83 ms using a neural net after these frequencies are found using Fourier analysis.

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Cited By

Costa LAl-Kiswany SRipeanu MYang H(2019)Support for Provisioning and Configuration Decisions for Data Intensive WorkflowsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2015.249769327:9(2725-2739)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1109/TPDS.2015.2497693
Carns PHarms KJenkins JMubarak MRoss RCarothers C(2016)Impact of data placement on resilience in large-scale object storage systems2016 32nd Symposium on Mass Storage Systems and Technologies (MSST)10.1109/MSST.2016.7897091(1-12)Online publication date: 2016
https://doi.org/10.1109/MSST.2016.7897091
Crume AMaltzahn CWard LKroeger TCurry M(2014)Automatic generation of behavioral hard disk drive access time models2014 30th Symposium on Mass Storage Systems and Technologies (MSST)10.1109/MSST.2014.6855553(1-11)Online publication date: Jun-2014
https://doi.org/10.1109/MSST.2014.6855553

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cover image ACM Conferences

PDSW '13: Proceedings of the 8th Parallel Data Storage Workshop

November 2013

55 pages

ISBN:9781450325059

DOI:10.1145/2538542

Conference Chairs:
Dean Hildebrand
IBM
,
Karsten Schwan
Georgia Tech

Copyright © 2013 ACM.

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Published: 17 November 2013

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SC13: International Conference for High Performance Computing, Networking, Storage and Analysis

November 17 - 21, 2013

Colorado, Denver

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PDSW '13 Paper Acceptance Rate 8 of 16 submissions, 50%;

Overall Acceptance Rate 17 of 41 submissions, 41%

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Cited By

Costa LAl-Kiswany SRipeanu MYang H(2019)Support for Provisioning and Configuration Decisions for Data Intensive WorkflowsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2015.249769327:9(2725-2739)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1109/TPDS.2015.2497693
Carns PHarms KJenkins JMubarak MRoss RCarothers C(2016)Impact of data placement on resilience in large-scale object storage systems2016 32nd Symposium on Mass Storage Systems and Technologies (MSST)10.1109/MSST.2016.7897091(1-12)Online publication date: 2016
https://doi.org/10.1109/MSST.2016.7897091
Crume AMaltzahn CWard LKroeger TCurry M(2014)Automatic generation of behavioral hard disk drive access time models2014 30th Symposium on Mass Storage Systems and Technologies (MSST)10.1109/MSST.2014.6855553(1-11)Online publication date: Jun-2014
https://doi.org/10.1109/MSST.2014.6855553

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